from sklearn.ensemble import RandomForestClassifier
def feature_extraction(image):
# Calculate GLCM for all angles
glcms = []
Theta = ["0", "pi/4", "pi/2", "3pi/4"]
theta = [0, np.pi/4, np.pi/2, 3*np.pi/4] # angles for GLCM
d = 1
for angle in theta:
glcm = graycomatrix(image, distances=[d], angles=[angle], levels=256, symmetric=True, normed=True)
glcms.append(glcm)
# Extract GLCM features
features = []
for glcm in glcms:
contrast = graycoprops(glcm, 'contrast')
dissimilarity = graycoprops(glcm, 'dissimilarity')
homogeneity = graycoprops(glcm, 'homogeneity')
energy = graycoprops(glcm, 'energy')
correlation = graycoprops(glcm, 'correlation')
ASM = graycoprops(glcm, 'ASM')
entropy = shannon_entropy(glcm)
row = [contrast[0][0], dissimilarity[0][0], homogeneity[0][0], ASM[0][0], energy[0][0], correlation[0][0], entropy]
features.append(row)
# Fit Random Forest Classifier and extract feature importances
X = np.array(features)
y = np.array([0, 1, 2, 3]) # labels for each angle
clf = RandomForestClassifier(n_estimators=100, random_state=42)
clf.fit(X, y)
feature_importances = clf.feature_importances_
# Print extracted features
print("Extracted Features:")
headers = ["Angle", "Contrast", "Dissimilarity", "Homogeneity", "ASM", "Energy", "Correlation", "Entropy"]
table = PrettyTable(headers)
i = 0
for glcm in glcms:
row = [Theta[i], features[i][0], features[i][1], features[i][2], features[i][3], features[i][4], features[i][5], features[i][6]]
table.add_row(row)
i += 1
print(table)
# Print selected features
print("\nSelected Features:")
headers = ["Feature", "Importance"]
table = PrettyTable(headers)
feature_names = ["Contrast", "Dissimilarity", "Homogeneity", "ASM", "Energy", "Correlation", "Entropy"]
for i in range(len(feature_names)):
row = [feature_names[i], feature_importances[i]]
table.add_row(row)
print(table)
# Return selected features
selected_features = X[:, feature_importances.argsort()[::-1][:3]] # Select top 3 features
